Mikelsons, G;
Mehrotra, A;
Musolesi, M;
Shadbolt, N;
(2019)
Evaluating machine learning algorithms for prediction of the adverse valence index based on the photographic affect meter.
In:
Proceedings of the 5th ACM Workshop on Mobile Systems for Computational Social Science.
(pp. pp. 5-10).
ACM
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Abstract
In recent years, numerous studies have explored the use of machine learning algorithms for supporting applications in social and clinical psychology. In particular, there is an increasing prevalence of smartphone-based techniques for collecting data through embedded sensors and efficient in-situ questionnaires. Models are then built to explore the patterns between these data types. In this paper, we study the application of machine learning for the task of predicting mental states of adverse valence, based on the Photographic Affect Meter data. We present a technique for daily aggregation, which is designed to detect significant negative events. A variety of features is used as input, including GPS-based metrics and features assessing social interactions, sleep and phone usage. Experimental evidence is presented, which suggests that machine learning algorithms could successfully be employed for such a prediction task.
| Type: | Proceedings paper |
|---|---|
| Title: | Evaluating machine learning algorithms for prediction of the adverse valence index based on the photographic affect meter |
| Event: | MobiSys '19: The 17th Annual International Conference on Mobile Systems, Applications, and Services |
| Location: | Seoul, Republic of Korea |
| Dates: | 21st June 2019 |
| ISBN-13: | 978-1-4503-6777-6 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3325426.3329948 |
| Publisher version: | https://doi.org/10.1145/3325426.3329948 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10092095 |
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